Deepening Insights from Learning Analytics through Student Perspectives
- Conference
- Location
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Montreal, Quebec, Canada
- Publication Date
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June 22, 2025
- Start Date
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June 22, 2025
- End Date
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August 15, 2025
- Conference Session
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DSAI Technical Session 8: Learning Analytics and Data-Driven Instruction
- Tagged Division
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Data Science and Artificial Intelligence (DSAI) Constituent Committee
- Page Count
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11
- Permanent URL
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https://peer.asee.org/56212
Paper Authors
Selena Johnson Rowan University
Selena Johnson is a senior in the Department of Mechanical Engineering at Rowan University. She earned her Associate’s degree in Engineering Science with a Mathematics minor from Rowan College of South Jersey. Her interests include innovation and engineering education, as well as developing optimized solutions that enhance system efficiency and streamline processes.
Paromita Nath
Rowan University
orcid.org/0000-0001-8955-5579
Dr. Paromita Nath is an Assistant Professor in Mechanical Engineering at Rowan University. She earned her Ph.D. in Civil Engineering from Vanderbilt University. She is passionate about advancing engineering education through machine learning and data analysis, building on her expertise in uncertainty quantification, Bayesian inference, process design and control under uncertainty, and probabilistic digital twin. Her research spans diverse applications, including additive manufacturing and public health.
Smitesh Bakrania
Rowan University
orcid.org/0000-0003-0663-0241
His research interests include combustion synthesis of nanoparticles and combustion catalysis using nanoparticles. He is currently involved in developing educational apps for instructional and research purposes.
Abstract
Online learning generates student interaction data in learning management systems (LMS) that can provide engagement insights. However, traditional learning analytics often lacks the context behind student behaviors, limiting the effectiveness of interventions. In this work-in-progress at Rowan University, an analysis of asynchronous online courses identified LMS-captured behaviors such as skipping videos and rewatching content. To gain deeper insights from the data, interviews with former students were conducted to explore context by highlighting factors such as distractions, preconceptions, and instructor feedback. Analysis of the student interview data suggests that course design, instructor feedback, and content delivery influence student engagement in online courses. Integrating LMS-based learning analytics data with student perspectives has the potential for educators to create engaging, student-centered online environments that bridge skill gaps, improve learning experiences, and better address student needs for success.
Johnson, S., & Nath, P., & Bakrania, S. (2025, June), Deepening Insights from Learning Analytics through Student Perspectives Paper presented at 2025 ASEE Annual Conference & Exposition , Montreal, Quebec, Canada . https://peer.asee.org/56212
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